Method and electronic system for predicting at least one fitness value of a protein via an extended numerical sequence, related computer program product

ABSTRACT

This method for predicting at least one fitness value of a protein is implemented on a computer and include: calculating Q elementary numerical sequences, Q being an integer greater than or equal to 2, each elementary numerical sequence depending on a respective encoding of the amino acid sequence of the protein according to a protein database; determining an extended numerical sequence by concatenating the Q elementary numerical sequences; for each fitness: comparing the determined extended numerical sequence with reference extended numerical sequences of a predetermined database, the database containing reference extended numerical sequences for different values of the fitness; and predicting a value of the fitness according to the comparing.

The present invention concerns a method and a related electronic system for predicting at least one fitness value of a protein, the protein comprising an amino acid sequence. The invention also concerns a computer program product including software instructions which, when implemented by a computer, implement such a method.

BACKGROUND OF THE INVENTION

Proteins are biological molecules consisting of at least one chain of amino acids sequence. Proteins differ from one another primarily in their sequence of amino acids, the differences between sequences being called “mutations”.

One of the ultimate goals of protein engineering is the design and construction of peptides, enzymes, proteins or amino acid sequences with desired properties (collectively called “fitness”). The construction of modified amino acid sequences with engineered amino acid substitutions, deletions or insertions of amino acids or blocks of amino acids (chimeric proteins) (i.e. “mutants”) allows an assessment of the role of any particular amino acid in the fitness and an understanding of the relationships between the protein structure and its fitness.

The main objective of the quantitative structure-function/fitness relationship analysis is to investigate and mathematically describe the effect of the changes in structure of a protein on its fitness. The impact of mutations is related to physico-chemical and other molecular properties of varying amino acids and can be approached by means of statistical analysis.

Exploring the fitness landscape, investigating all possible combinations (permutations) of n single point substitutions is a very difficult task. Indeed, the number of mutants increases very quickly (Table 1).

TABLE 1 Number of possible mutants for n mutations No of single point mutations No of mutants 2 4 4 16 6 64 8 256 10 1024 12 4096 14 16384 16 65536 40 1.1 × 10¹²

Exploring all possible mutants is difficult experimentally, in particular when n increases. In practice, it is quite easy and cheap to produce mutants with single point substitutions in wet lab. For each of them, fitness can be readily characterized.

But combining single point substitutions is not so easy in wet lab. Generating all possible (>=2^(n)) combinations of targeted n single point substitutions can be very fastidious and costly. Evaluating fitness on large scale is problematic.

Mixed in vitro and in silico approaches have been developed to assist the process of directed evolution of proteins. They require from the wet lab to construct a library of mutants (by site-directed, random, or combinatorial mutagenesis), to retrieve the sequences and/or structures of a limited number of samples from library (called the “learning data set”) and to assess fitness of each sampled mutant. They further require from the in silico to extract descriptors for each mutant, to use multivariate statistical method(s) for establishing relationship between descriptors and fitness (learning phase) and to establish a model to make predictions for mutants which are not experimentally tested.

A method based on 3D structure called Quantitative structure-function relationships (QFSR) has been proposed (Damborsky J, Prot. Eng. (1998) January; 11(1):21-30). Other methods, based only on sequence, not on 3D structure, and performing in silico rational screening using statistical modelling were proposed (Fox R. et al., Protein Eng. (2003) 16(8):589-97; Fox R., Journal of Theoretical Biology (2005), 234:187-199; Minshull J. et al., Curr Opin Chem Biol. 2005 April; 9(2):202-9; Fox R. et al., Nature Biotechnology (2007), 25(3):338-344; Fox R. and Huisman G W Trends Biotechnol. 2008 March; 26(3):132-8). The most known is ProSAR (Fox R., Journal of Theoretical Biology (2005), 234:187-199; Fox R. et al., Nature Biotechnology (2007), 25(3):338-344) which is based on a binary encoding (0 or 1).

The QSFR method is efficient and takes into account information about possible interactions with non-variants residues. However, QSFR needs information on 3D protein structure, which is still currently limited, and the method is furthermore slow.

Comparatively, ProSAR does not need knowledge of 3D structure as it computed based on primary sequence only and can use linear and non-linear models. However, ProSAR still suffers from drawbacks and its capacity of screening is limited. In particular, only those residues undergoing variation are included in the modelling and, as a consequence, information about possible interactions between mutated residues and other non-variant residues are missing. ProSAR relies on binary encoding (0 or 1) of the mutations which does not take into account the physico-chemical or other molecular properties of the amino acids. Additionally, (i) the new sequences that can be tested are only sequences with mutations, or combinations of mutations, at the positions that were used in the learning set used to build the model; (ii) the number of positions of mutations in the new sequences to be screened cannot be different from the number of mutations in the train set; and (iii) the calculation time when introducing non-linear terms in order to build a model is very long on a super computer (up to 2 weeks for 100 non-linear terms).

A versatile and fast in silico approach to help in the process of directed evolution of proteins is therefore still needed. The invention provides a method fulfilling these requirements and which is based on Digital Signal Processing (DSP).

Digital Signal Processing techniques are analytic procedures, which decompose and process signals in order to reveal information embedded in them. The signals may be continuous (unending), or discrete such as the protein residues. In proteins, Fourier transform methods have been used for biosequence (DNA and protein) comparison, characterization of protein families and pattern recognition, classification and other structure based studies such as analysis of symmetry and repeating structural units or patterns, prediction of secondary/tertiary structure prediction, prediction of hydrophobic core, motifs, conserved domains, prediction of membrane proteins, prediction of conserved regions, prediction of protein subcellular location, for the study of secondary structure content in amino acids sequence and for the detection of periodicity in protein. More recently new methods for the detection of solenoids domains in protein structures were proposed.

Digital Signal Processing techniques have helped analyse protein interactions (Cosic I., IEEE Trans Biomed Eng. (1994) 41(12):1101-14) and made biological functionalities calculable. These studies have been reviewed in detail in Nwankwo N. and Seker H. (J Proteomics Bioinform (2011) 4(12): 260-268).

In these approaches, protein residues are first converted into numerical sequences using one of the available AAindex from the database AAindex (Kawashima, S. and Kanehisa, M. Nucleic Acids Res. (2000), 28(1):374; Kawashima, S. et al., Nucleic Acids Res. January 2008; 36), representing a biochemical property or physico-chemical parameter for each amino acid. These numerical sequences are then processed by means of Discrete Fourier Transform (DFT) to present the biological characteristics of the proteins in the form of Informational Spectrum. This procedure is called Informational Spectrum Method (ISM) (Veljkovic V, et al., IEEE Trans Biomed Eng. 1985 May; 32(5):337-41). ISM procedure has been used to investigate principal arrangement in Calcium binding protein (Viari A, et al., Comput Appl Biosci. 1990 April; 6(2):71-80) and Influenza viruses (Veljkovic V., et al. BMC Struct Biol. 2009 Apr. 7; 9:21, Veljkovic V., et al. BMC Struct Biol. 2009 Sep. 28; 9:62).

A variant of the ISM, which engages amino acids parameter called Electron-Ion Interaction Potential (EIIP) is referred as Resonant Recognition Model (RRM). In this procedure, biological functionalities are presented as spectral characteristics. This physico-mathematical process is based on the fact that biomolecules with same biological characteristics recognise and bio-attach to themselves when their valence electrons oscillate and then reverberate in an electromagnetic field (Cosic I., IEEE Trans Biomed Eng. (1994) 41(12):1101-14; Cosic I., The Resonant Recognition Model of Macromolecular Bioactivity Birkhauser Verlag, 1997).

The Resonant Recognition Model involves four steps (see Nwankwo N. and Seker H., J Proteomics Bioinform (2011) 4(12): 260-268):

-   -   Step 1: Conversion of the Protein Residues into Numerical Values         of Electron-Ion Interaction Potential (EIIP) Parameter.     -   Step 2: Zero-padding/Up-sampling. The process uses a zero         padding to fill the gaps in the sequence of the proteins to be         analysed at any position as signal processing requires that the         window length of all proteins be the same.     -   Step 3: processing of the Numerical Sequences using Fast Fourier         Transform (FFT) to yield Spectral Characteristics (SC) and         point-wise multiplied to generate the Cross Spectral (CS)         features during step 4.     -   Step 4: Cross-Spectral Analysis: Cross-Spectral (CS) analysis         represents the point-wise multiplication of the Spectral         Characteristics (SC).

Therefore, the CS analysis has been used qualitatively, to predict, for instance, ligand-receptor binding based on common frequencies (resonance) between the ligand and receptor spectra. Another example is to predict a ras-like activity or not, i.e. ability or not to transform cells, by applying the RRM to Ha-ras p21 protein sequence.

The information provided by these prior art methods are useful but are however insufficient to identify the most valuable protein mutants generated by directed evolution.

WO 2016/166253 A1 discloses a method and a related electronic system for predicting at least one fitness value of a protein, based on a protein spectrum, the protein spectrum being for example a Fourier Transform, such as a Fast Fourier Transform, applied to a numerical sequence obtained further to encoding the amino acid sequence of the protein.

The results provided by this last method are better than the ones provided by the other prior art methods.

However, the accuracy of the proteins fitness values predicted by this method may be further improved.

SUMMARY OF THE INVENTION

The invention therefore relates to a method for predicting at least one fitness value of a protein, the method being implemented on a computer and including the following steps:

-   -   calculating Q elementary numerical sequences, Q being an integer         greater than or equal to 2, each elementary numerical sequence         depending on a respective encoding of the amino acid sequence of         the protein according to a protein database,     -   determining an extended numerical sequence by concatenating the         Q elementary numerical sequences,

for each fitness:

-   -   comparing the determined extended numerical sequence with         reference extended numerical sequences of a predetermined         database, said database containing reference extended numerical         sequences for different values of said fitness,     -   predicting a value of said fitness according to the comparison         step.

According to other advantageous aspects of the invention, the method comprises one or more of the following features taken alone or according to all technically possible combinations:

-   -   at least one elementary numerical sequence is an elementary         protein spectrum, the elementary protein spectrum being obtained         by applying a Fourier Transform to an intermediate numerical         sequence, the intermediate numerical sequence being obtained by         a respective encoding of the amino acid sequence of the protein,

the Fourier Transform being preferably a Fast Fourier Transform,

at least one elementary protein spectrum being preferably calculated for said amino acid sequence according to a given set of frequency or frequencies;

-   -   each elementary protein spectrum depends on the following         equation:

$f_{j} = {\sum\limits_{k = 0}^{N - 1}{x_{k} \cdot {\exp\left( {{\frac{{- 2}i\pi}{N} \cdot j}{\cdot k}} \right)}}}$

-   -   where j is an index-number of the elementary protein spectrum         f_(j);     -   the intermediate numerical sequence includes N value(s) denoted         x_(k), with 0≤k≤N−1     -   and N≥1; and     -   i defining the imaginary number such that i²=−1;     -   the protein database includes at least one index of numerical         values, each numerical value being given for a respective amino         acid; and     -   wherein each encoding of the amino acid sequence of the protein         is performed for a respective index, the value in the numerical         sequence for each amino acid being equal to the numerical value         for said amino acid in the respective index;     -   all the elementary numerical sequences are distinct from each         other;     -   among a pair of elementary numerical sequences, one differs from         the other further to the applying of the Fourier Transform for         only one elementary numerical sequence of the pair and/or         further to a different index from the one to the other         elementary numerical sequence of the pair;     -   the protein database includes several indexes of numerical         values, and     -   wherein the method further includes a step of:         -   selecting the best index(es) based on a comparison of             measured fitness values for sample proteins with predicted             fitness values previously obtained for said sample proteins             according to each index;         -   at least one encoding of the amino acid sequence of the             protein being then performed using a respective selected             index;     -   during the selection step, the selected index(es) are the         index(es) with the smallest root mean square error(s),     -   wherein the root mean square error for each index verifies the         following equation:

${RMSE_{Index_{-}j}} = \sqrt{\sum\limits_{i = 1}^{S}\frac{\left( {y_{i} - {\overset{\hat{}}{y}}_{i,j}} \right)^{2}}{S}}$

-   -   where y_(i) is the measured fitness of the i^(th) sample         protein,     -   ŷ_(i,j) is the predicted fitness of the i^(th) sample protein         with the j^(th) index, and     -   S the number of sample proteins;     -   during the selection step, the selected index(es) are the         index(es) with the coefficient(s) of determination nearest to 1,     -   wherein the coefficient of determination for each index verifies         the following equation:

$R_{{Index}\_ j}^{2} = \frac{\left( {\sum\limits_{i = 1}^{S}{\left( {y_{i} - \overset{\_}{y}} \right)\left( {{\overset{\hat{}}{y}}_{i,j} - \overset{\_}{\overset{\hat{}}{y}}} \right)}} \right)^{2}}{\underset{i = 1}{\sum\limits^{S}}{\left( {y_{i} - \overset{\_}{y}} \right)^{2}{\underset{i = 1}{\sum\limits^{S}}\left( {{\overset{\hat{}}{y}}_{i,j} - \overset{\_}{\overset{\hat{}}{y}}} \right)^{2}}}}$

-   -   where y_(i) is the measured fitness of the i^(th) sample         protein,     -   ŷ_(i,j) is the predicted fitness of the i^(th) sample protein         with the j^(th) index,     -   S the number of sample proteins,     -   y is an average of the measured fitness for the S sample         proteins, and     -   {circumflex over (y)} is an average of the predicted fitness for         the S sample proteins;     -   during the determining step, the elementary numerical sequences         are concatenated according to a concatenation pattern for         determining the extended numerical sequence, the reference         extended numerical sequences having being obtained with the same         concatenation pattern;     -   the concatenation pattern defines, for each elementary numerical         sequence from the succession of the elementary numerical         sequences to be concatenated, the respective index and the         applying or not of the Fourier Transform;     -   the protein database includes several indexes classified into         distinct categories, and the concatenation pattern includes         indexes from at least two categories;     -   each category being preferably a family associated to a protein         feature, such as a protein feature chosen from among the group         consisting of: alpha & turn propensities, beta propensity,         composition, hydrophobicity, physicochemical property and other         protein property; or     -   each category being preferably a cluster of index(es), the         clusters being obtained according to statistical feature(s) of         the indexes; and     -   the comparison step comprises identifying, in the predetermined         database of reference extended numerical sequences for different         values of said fitness, the reference extended numerical         sequence which is the closest according to a predetermined         criterion to the determined extended numerical sequence, the         predicted value of said fitness being then equal to the fitness         value which is associated in said database with the identified         reference extended numerical sequence.

The invention also relates to a computer program product including software instructions which, when implemented by a computer, implement a method as defined above.

The invention also relates to an electronic prediction system for predicting at least one fitness value of a protein, the prediction system including:

-   -   a calculation module configured for calculating Q elementary         numerical sequences, Q being an integer greater than or equal to         2, each elementary numerical sequence depending on a respective         encoding of the amino acid sequence of the protein according to         a protein database,     -   a determination module configured for determining an extended         numerical sequence by concatenating the Q elementary numerical         sequences,     -   a prediction module configured for, for each fitness:         -   comparing the determined extended numerical sequence with             reference extended numerical sequences of a predetermined             database, said database containing reference extended             numerical sequences for different values of said fitness,         -   predicting a value of said fitness according to said             comparison.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be better understood upon reading of the following description, which is given solely by way of example and with reference to the appended drawings, in which:

FIG. 1 is a schematic view of an electronic prediction system for predicting at least one fitness value of a protein, the prediction system including a calculation module for calculating Q elementary numerical sequences, Q≥2, each one depending on a respective encoding of the amino acid sequence of the protein according to a protein database, a determination module for determining an extended numerical sequence by concatenating the Q elementary numerical sequences, and a prediction module configured for predicting at least one value of each fitness;

FIG. 2 is a schematic flow chart of a prediction method for predicting at least one fitness value of a protein, according to the invention;

FIG. 3 is a set of points illustrating predicted and measured values of the thermostability for a set of proteins of the cytochrome P450 family, each point being related to a respective protein with the ordinate corresponding to the predicted value and the abscissa corresponding to the measured value, with a prior art prediction method;

FIG. 4 is a view similar to that of FIG. 3, with a prediction method according to the invention;

FIG. 5 is a view similar to that of FIG. 3 illustrating predicted and measured values of the potency for a set of GLP2 mutants, with a prior art prediction method;

FIG. 6 is a view similar to that of FIG. 5, with a prediction method according to the invention;

FIGS. 7 and 8 are views similar to that of FIG. 3 illustrating predicted and measured values of the enantioselectivity for a set of proteins of an epoxide hydrolase family, with a prior art prediction method;

FIG. 9 is a view similar to that of FIGS. 7 and 8, with a prediction method according to the invention;

FIG. 10 is a view similar to that of FIG. 3 illustrating predicted and measured values of the binding affinity for a set of TNF mutants, with a prior art prediction method;

FIG. 11 is a view similar to that of FIG. 10, with a prediction method according to the invention;

FIGS. 12 to 14 are views similar to that of FIG. 4, for other examples of respective encoding indexes used for encoding of the amino acid sequence of the protein into elementary numerical sequences;

FIG. 15 is a view similar to that of FIG. 6, for another example of respective encoding indexes issued from different categories of index(es);

FIG. 16 is a view similar to that of FIG. 11, for another example of respective encoding indexes issued from different clusters of index(es);

FIG. 17 is a view illustrating the different clusters of index(es) used in the example of FIG. 16;

FIG. 18 is a view similar to that of FIG. 11, for another example of respective encoding indexes;

FIG. 19, and respectively 20, are views similar to that of FIG. 10, with a prior art prediction method, for a first encoding index, and respectively for a second encoding index;

FIG. 21 is a view similar to that of FIG. 11, with a prediction method according to the invention, using the first and second encoding indexes of FIGS. 19 and 20;

FIG. 22 is a view similar to that of FIG. 3, with a prediction method according to the invention, using an elementary protein spectrum for a given set of frequencies or harmonics; and

FIGS. 23 and 24 are views similar to that of FIG. 22, with a prediction method according to the invention, wherein a respective elementary numerical sequence is an elementary protein spectrum for a given set of frequency(ies) or harmonic(s).

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

By “protein”, as used herein, is meant at least 2 amino acids linked together by a peptide bond. The term “protein” includes proteins, oligopeptides, polypeptides and peptides. The peptidyl group may comprise naturally occurring amino acids and peptide bonds, or synthetic peptidomimetic structures, i.e. “analogs”, such as peptoids. The amino acids may either be naturally occurring or non-naturally occurring. In preferred embodiments, a protein comprises at least 10 amino acids, but less amino acids can be managed.

The “fitness” of a protein refers to its adaptation to a criterion, such as catalytic efficacy, catalytic activity, kinetic constant, Km, Keq, binding affinity, thermostability, solubility, aggregation, potency, toxicity, allergenicity, immunogenicity, thermodynamic stability, flexibility, protein expression level and mRNA expression level. According to the invention, the “fitness” is also called “activity” and it will be considered in the following of the description that the fitness and the activity refer to the same feature.

The catalytic efficacy is usually expressed in s⁻¹·M⁻¹ and refers to the ratio kcat/Km.

The catalytic activity is usually expressed in mol·s⁻¹ and refers to the enzymatic activity level in enzyme catalysis.

The kinetic constant kcat is usually expressed in s⁻¹ and refers to the numerical parameter that quantifies the velocity of a reaction.

The Km is usually expressed in M and refers to the substrate concentration at which the velocity of reaction is half its maximum.

The Keq is usually expressed in (M, M⁻¹ or no unit) and quantity characterizing a chemical equilibrium in a chemical reaction.

The binding affinity is usually expressed in M and refers to the strength of interactions between proteins or proteins and ligand (peptide or small chemical molecule).

The thermostability is usually expressed in ° C. and usually refers to the measured activity T₅₀ defined as the temperature at which 50% of the protein is irreversibly denatured after incubation time of 10 minutes.

The solubility is usually expressed in mol/L and refers to the number of moles of a substance (the solute) that can be dissolved per liter of solution before the solution becomes saturated.

The aggregation is usually expressed using aggregation Index (from a simple absorption measurement at 280 nm and 340 nm) and refers to the biological phenomenon in which mis-folded protein aggregate (i.e., accumulate and clump together) either intra- or extracellularly.

The potency is usually expressed in M and refers to the measure of drug activity expressed in terms of the amount required to produce an effect of given intensity.

The toxicity is usually expressed in M and refers to the degree to which a substance (a toxin or poison) can harm humans or animals.

The allergenicity is usually expressed in Bioequivalent Allergy Unit per mL (BAU/mL) and refers to the capacity of an antigenic substance to produce immediate hypersensitivity (allergy).

The immunogenicity is usually expressed as the unit of the amount of antibody in a sample and refers to the ability of a particular substance, such as an antigen or epitope, to provoke an immune response in the body of a human or animal.

The stability is usually expressed as ΔΔG (kcal/mol−1) and refers to thermodynamic stability of a protein that unfolds and refolds rapidly, reversibly, and cooperatively.

The flexibility is usually expressed in A° and refers to protein disorder and conformational changes.

The protein expression level is usually expressed as a unit-less value, such as a percentage or a decimal value, and refers to the amount of production of proteins by cells.

The mRNA expression level is also usually expressed as a unit-less value, such as a percentage or a decimal value, and refers to the quantity of functional copies of mRNA in living cells.

The enantioselectivity refers to the preferential formation of one stereoisomer over another in a chemical reaction, or to the selectivity of a reaction towards one of a pair of enantiomers. The enantioselectivity is usually expressed by an E-value which is transformable in ΔΔG‡ (kcal/mol) by the relation ΔΔG‡=−RT In (E).

In FIG. 1, an electronic prediction system 10 for predicting at least one fitness value of a protein includes a calculation module 20 configured for calculating Q elementary numerical sequences, Q being an integer greater than or equal to 2, each elementary numerical sequence depending on a respective encoding of the amino acid sequence of the protein according to a protein database 21.

The electronic prediction system 10 further includes a determination module 22 configured for determining an extended numerical sequence Ext_SEQ by concatenating the Q elementary numerical sequences.

In optional addition, the electronic prediction system 10 includes a modeling module 24 configured for predetermining a reference database 25, said reference database 25 containing reference extended numerical sequences for different values of said fitness.

The electronic prediction system 10 further includes a prediction module 26 configured for, for each fitness, comparing the determined extended numerical sequence Ext_SEQ with reference extended numerical sequences of the reference database 25, and predicting a value of said fitness according to said comparison.

In optional addition, the electronic prediction system 10 includes a screening module 28 configured for analyzing the protein according to the determined extended numerical sequence Ext_SEQ, thereby for screening mutants' libraries, the analysis being for example a factorial discriminant analysis or a principal component analysis.

In the example of FIG. 1, the electronic prediction system 10 includes a data processing unit 30, a display screen 32 and input means 34 for inputting data into the data processing unit 30.

The data processing unit 30 is, for example, made of a memory 40 and a processor 42 associated to the memory 40.

The display screen 32 and the input means 34 are known per se.

In the example of FIG. 1, the calculation module 20, the determination module 22 and the prediction module 26, and in optional addition the modeling module 24 and/or the screening module 28, are for example each realized, i.e. implemented, as a software executable by the processor 42. The memory 40 of the processing unit 30 is adapted to store a calculation software configured for calculating Q elementary numerical sequences; a determination software configured for determining the extended numerical sequence Ext_SEQ by concatenating the Q elementary numerical sequences; and a prediction software configured for, for each fitness, comparing the determined extended numerical sequence Ext_SEQ with reference extended numerical sequences of the reference database 25 and predicting a value of said fitness according to said comparison. In optional addition, the memory 40 of the processing unit 30 is adapted to store a modeling software configured for predetermining the reference database 25 containing reference extended numerical sequences for different values of said fitness; and/or a screening software configured for analyzing the protein according to the determined extended numerical sequence Ext_SEQ, thereby for screening mutants' libraries. The processor 42 of the processing unit 30 is then configured to execute the calculation software, the determination software and the prediction software, and in optional addition the modeling software and/or the screening software.

As a variant not shown, the calculation module 20, the determination module 22 and the prediction module 26, and in optional addition the modeling module 24 and/or the screening module 28, are each in the form of a programmable logic component, such as a Field Programmable Gate Array or FPGA, or in the form of a dedicated integrated circuit, such as an Application Specific integrated Circuit or ASIC.

When the electronic prediction system 10 is in the form of one or more software programs, i.e. in the form of a computer program, it is also capable of being recorded on an computer-readable medium, not shown. The computer-readable medium is, for example, a medium capable of storing electronic instructions and being coupled to a bus of a computer system. For example, the readable medium is an optical disk, a magneto-optical disk, a ROM memory, a RAM memory, any type of non-volatile memory (for example EPROM, EEPROM, FLASH, NVRAM), a magnetic card or an optical card. A computer program with software instructions is then stored on the readable medium.

The calculation module 20 is configured for calculating several elementary numerical sequences, each elementary numerical sequence depending on a respective encoding of the amino acid sequence of the protein according to the protein database 21.

The calculation module 20 is for example adapted for encoding the amino acid sequence into respective elementary numerical sequence(s) according to the protein database 21, each elementary numerical sequence comprising a value x_(k) for each amino acid of the sequence. The elementary numerical sequence is constituted of P value(s) x_(k), with 0≤k≤P−1 and P≥1, k and P being integers.

In other words, encoding the amino acid sequence into a numerical sequence results in replacing each letter of amino acid in the amino acid sequence by a value.

The skilled person will notice that the amino acid sequence corresponds to the whole amino acid sequence of the protein or alternatively only to a partial amino acid sequence of the protein. According to this alternative, the partial amino acid sequence corresponds in other words to only one or several amino acid positions among the whole amino acid sequence of the protein.

The protein database 21 corresponds in a general manner to a set of relationship(s), wherein each relationship associates any numerical value to a given amino acid.

The protein database 21 is, for example, stored in the memory 40. Alternatively, the protein database 21 is stored in a remote memory, not shown, which is distinct from the memory 40.

The protein database 21 is for example the Amino Acid Index Database, also called AAIndex. Amino Acid Index Database is available from http://www.genome.jp/dbget-bin/wwwz_bfind?aaindex (version Release 9.1, August 6). The AAIndex holds 566 indexes representing various physicochemical and biochemical properties for the 20 standard amino acids; and correlations between these indices are also listed in the AAIndex.

Alternatively, the protein database 21 contains predefined arbitrary numerical values, for example that range from 1 to N_(AA), where N_(AA) is the number of natural and/or non-natural amino acids in the protein database 21.

Further alternatively, the protein database 21 contains calculated numerical values for each amino acid, wherein these numerical values are calculated according to predefined calculation law or calculated randomly or pseudo-randomly.

Alternatively, or in addition, the protein database 21 contains numerical values for non-natural amino acids. The protein database 21 is for example based on the article “An index for characterization of natural and non-natural amino acids for peptidomimetics” from Liang, G., Liu, Y., Shi, B., Zhao, J., & Zheng, J., published in PloS one, 8(7), e67844, in 2013 and from the utilization of the application e-dragon, available from http://www.vcclab.org/lab/edragon which allows the calculation of physicochemical molecular descriptors from given molecules. The protein database 21 contains accordingly for example 615 non-natural amino acids with more of 1600 descriptors.

The protein database 21 includes at least one index of numerical values, each value being given for a respective amino acid. The protein database 21 includes preferably several indexes of numerical values.

The protein database 21 includes for example one or several indexes of biochemical or physico-chemical property values, each property value being given for a respective amino acid. Each index corresponds for example AAindex code, as it will be illustrated in the following in light of the respective examples. The chosen AAindex codes for encoding the amino acid sequence are for example: D Normalized frequency of extended structure, D Electron-ion interaction potential values, D SD of AA composition of total proteins, D pK-C or D Weights from the IFH scale.

In optional addition, when the protein database 21 includes several indexes of numerical values, these several indexes are for example classified into distinct categories. According to a classification example, each category is a family associated to a protein feature, such as a protein feature chosen from among the group consisting of: alpha & turn propensities, beta propensity, composition, hydrophobicity, physicochemical property and other protein property. According to another classification example, each category is a cluster of index(es) which is obtained according to statistical feature(s) of the indexes. FIG. 17 illustrates such a classification example with eight clusters C1 to C8.

For encoding the amino acid sequence, the calculation module 20 is then adapted to determine, for each amino acid, the numerical value for said amino acid according to the given index, each encoded value x_(k) in the elementary numerical sequence being then equal to a respective numerical value.

In optional addition, when the protein database 21 includes several indexes of numerical values; the calculation module 20 is for example configured for selecting the best index based on a comparison of measured fitness values for sample proteins with predicted fitness values previously obtained for said sample proteins according to each index; and then for encoding the amino acid sequence using the selected index.

The selected index is, for example, the index with the smallest root mean square error, wherein the root mean square error for each index verifies the following equation:

$\begin{matrix} {{RMSE_{Index_{-}j}} = \sqrt{\sum\limits_{i = 1}^{S}\frac{\left( {y_{i} - {\overset{\hat{}}{y}}_{i,j}} \right)^{2}}{S}}} & (1) \end{matrix}$

-   -   where y_(i) is the measured fitness of the i^(th) sample         protein,     -   ŷ_(i,j) is the predicted fitness of the i^(th) sample protein         with the j^(th) index, and     -   S the number of sample proteins.

Alternatively, the selected index is the index with the coefficient of determination nearest to 1, wherein the coefficient of determination for each index verifies the following equation:

$\begin{matrix} {R_{{Index}\_ j}^{2} = \frac{\left( {\underset{i = 1}{\sum\limits^{S}}{\left( {y_{i} - \overset{\_}{y}} \right)\left( {{\overset{\hat{}}{y}}_{i,j} - \overset{\_}{\overset{\hat{}}{y}}} \right)}} \right)^{2}}{\sum\limits_{i = 1}^{S}{\left( {y_{i} - \overset{\_}{y}} \right)^{2}{\sum\limits_{i = 1}^{S}\left( {{\overset{\hat{}}{y}}_{i,j} - \overset{\_}{\overset{\hat{}}{y}}} \right)^{2}}}}} & (2) \end{matrix}$

-   -   where y_(i) is the measured fitness of the i^(th) sample         protein,     -   ŷ_(i,j) is the predicted fitness of the i^(th) sample protein         with the j^(th) index,     -   S the number of sample proteins,     -   y is an average of the measured fitness for the S sample         proteins, and     -   {circumflex over (y)} is an average of the predicted fitness for         the S sample proteins.

In optional addition, the calculation module 20 is further configured for normalizing the obtained elementary numerical sequence, for example by subtracting to each value x_(k) of the elementary numerical sequence a mean x of the elementary numerical sequence values.

In other words, each normalized value, denoted {tilde over (x)}_(k), verifies the following equation:

{tilde over (x)} _(k) =x _(k) −k   (3)

The mean x is, for example, an arithmetic mean and satisfies:

$\begin{matrix} {\overset{\_}{x} = {\frac{1}{P} \times {\sum\limits_{k = 0}^{P - 1}x_{k}}}} & (4) \end{matrix}$

Alternatively, the mean x is a geometric mean, a harmonic mean or a quadratic mean.

In optional addition, the calculation module 20 is further configured for zero-padding the obtained elementary numerical sequence by adding M zeros at one end of said elementary numerical sequence, with M equal to (N−P) where N is a predetermined integer and P is the initial number of values in said elementary numerical sequence. N is therefore the total number of values in the elementary numerical sequence after zero-padding.

In optional addition, at least one elementary numerical sequence is an elementary protein spectrum, the elementary protein spectrum being obtained by applying a Fourier Transform, such as a Fast Fourier Transform, to an intermediate numerical sequence, the intermediate numerical sequence being obtained by a respective encoding of the amino acid sequence of the protein.

According to this optional addition, the calculation module 20 is configured for calculating the elementary protein spectrum according to the intermediate numerical sequence.

The calculated elementary protein spectrum includes at least one frequency value. The elementary protein spectrum is therefore calculated for the whole frequency spectrum or alternatively only according to a given set of frequency(ies) or harmonic(s) with one or several frequency values. This alternative with the elementary protein spectrum calculated only according to a given set of frequency(ies) or harmonic(s) will be further described later in view of the examples of FIGS. 22 to 24.

For determining the set of frequency(ies) or harmonic(s), i.e. for selecting frequency(ies) or harmonic(s), the calculation module 20 is for example configured for using a filter method or a wrapper method.

A filter method selects variables regardless of the model and is for example based only the correlation with the variable to predict. A filter method suppresses the least interesting variables. The other variables will be part of a classification or a regression model used to classify or to predict data. Such a filter method is for example carried out by correlating amplitude values at each harmonic with activity values (i.e. the values to be predicted), and then for selecting the harmonic(s) with the highest correlation. The correlation is for example evaluated according to the R2 and the set of frequency(ies) or harmonic(s) is then a given percentage frequency(ies) or harmonic(s) for which R2 is the highest.

A wrapper method evaluates subsets of variables which allows, unlike filter methods, to detect possible interactions between variables. Such a wrapper method is for example disclosed in the article from T. M. Phuong, Z. Lin et R. B. Altman: “Choosing SNPs using feature selection” in IEEE Computational Systems Bioinformatics Conference, pages 301-309, (2005).

The calculation module 20 is configured for calculating the elementary protein spectrum f_(j), preferably by applying a Fourier Transform, such as a Fast Fourier Transform, to the obtained intermediate numerical sequence.

Each elementary protein spectrum f_(j) therefore verifies, for example, the following equation:

$\begin{matrix} {f_{j} = {\Sigma_{k = 0}^{P - 1}{x_{k} \cdot {\exp\left( {\frac{{- 2}i\pi}{P} \cdot j \cdot k} \right)}}}} & (5) \end{matrix}$

-   -   where j is an index-number of the elementary protein spectrum         f_(j); and     -   i defines the imaginary number such that i²=−1.

In optional addition, when the intermediate numerical sequence is normalized, the calculation module 20 is further configured for performing the elementary protein spectrum calculation on the normalized intermediate numerical sequence.

In other words, in this case, each elementary protein spectrum f_(j) therefore verifies, for example, the following equation:

$\begin{matrix} {f_{j} = {\Sigma_{k = 0}^{P - 1}{{\overset{\sim}{x}}_{k} \cdot {\exp\left( {\frac{{- 2}i\pi}{P} \cdot j \cdot k} \right)}}}} & (6) \end{matrix}$

In optional addition, when zero-padding is performed on the intermediate numerical sequence, the calculation module 20 is further configured for calculating the elementary protein spectrum f_(j) on the intermediate numerical sequence obtained further to zero-padding.

In other words, in this case, each elementary protein spectrum f_(j) therefore verifies, for example, the following equation:

$\begin{matrix} {f_{j} = {\Sigma_{k = 0}^{N - 1}{x_{k} \cdot {\exp\left( {\frac{{- 2}i\pi}{N} \cdot j \cdot k} \right)}}}} & (7) \end{matrix}$

In optional addition, when both normalization and zero-padding are performed on the intermediate numerical sequence, the calculation module 20 is further configured for calculating the elementary protein spectrum f_(j) on the normalized intermediate numerical sequence obtained further to zero-padding.

In other words, in this case, each elementary protein spectrum f_(j) therefore verifies, for example, the following equation:

$\begin{matrix} {f_{j} = {\Sigma_{k = 0}^{N - 1}{{\overset{\sim}{x}}_{k} \cdot {\exp\left( {\frac{{- 2}i\pi}{N} \cdot j \cdot k} \right)}}}} & (8) \end{matrix}$

The determination module 22 is configured for determining the extended numerical sequence Ext_SEQ by concatenating the Q elementary numerical sequences.

In the extended numerical sequence Ext_SEQ determined by the determination module 22, all the elementary numerical sequences are distinct from each other.

For example, among a pair of elementary numerical sequences, one differs from the other further to the applying of the Fourier Transform for only one elementary numerical sequence of the pair. In the following of the description, an elementary numerical sequence obtained further to the applying of the Fourier Transform is denoted FFT_Seq for a single encoding index, or FFT_Seq_(j1), FFT_Seq_(j2), when several encoding indexes j1, j2 are taken into consideration. Conversely, an elementary numerical sequence obtained without applying the Fourier Transform is denoted noFFT_Seq for a single encoding index, or noFFT_Seq_(j1), noFFT_Seq_(j2), when several encoding indexes j1, j2 are taken into consideration.

In addition, or alternatively, among a pair of elementary numerical sequences, one differs for example from the other further to a different index from the one to the other elementary numerical sequence of the pair.

As an example, if the amino acid sequence of the protein is encoded according to only one encoding index, the determination module 22 is configured for determining the extended numerical sequence Ext_SEQ according to the following formulation:

Ext_SEQ=noFFT_Seq--FFT_Seq   (9)

where the symbol “--” between the two elementary numerical sequences noFFT_Seq and FFT_Seq represents the concatenation of these two elementary numerical sequences.

According to another example, if the amino acid sequence of the protein is encoded according to two distinct encoding indexes j1 and j2, the determination module 22 is configured for determining the extended numerical sequence Ext_SEQ according to the following possible alternative formulations:

Ext_SEQ=noFFT_Seq_(j1)--noFFT_Seq_(j2)   (10)

Ext_SEQ=FFT_Seq_(j1)--noFFT_Seq_(j2)   (11)

Ext_SEQ=noFFT_Seq_(j1)--FFT_Seq_(j2)   (12)

Ext_SEQ=FFT_Seq_(j1)--FFT_Seq_(j2)   (13)

The skilled person will naturally derive, from the aforementioned formulations, the possible alternative formulations of the extended numerical sequence Ext_SEQ in the case wherein the amino acid sequence of the protein is encoded according a number Nb_Index of distinct encoding indexes j1, j2, . . . , j_(Nb_Index) which is strictly greater than 2.

It should be noted that even if all the elementary numerical sequences are distinct from each other, all the elementary numerical sequences preferably correspond, for a given extended numerical sequence Ext_SEQ, to the same amino acid sequence of the protein. All the elementary numerical sequences therefore depend, for a given extended numerical sequence Ext_SEQ, on a single amino acid sequence of the protein. Indeed, the electronic prediction system 10 according to the invention aims at better predicting fitness value(s) of said amino acid sequence of the protein. In other words, the elementary numerical sequences differ from one another through the encoding index and/or through applying or not of the Fourier Transform.

The above formulations each represent a concatenation pattern for concatenating the elementary numerical sequences into the determined extended numerical sequence Ext_SEQ.

In other words, the concatenation pattern defines, for each elementary numerical sequence from the succession of the elementary numerical sequences to be concatenated, the respective index and the applying or not of the Fourier Transform.

The determination module 22 is configured for concatenating the Q elementary numerical sequences into the extended numerical sequence Ext_SEQ according to the concatenation pattern. The concatenation pattern is preferably a predefined concatenation pattern.

In optional addition, when the protein database 21 includes several indexes classified into distinct categories, the concatenation pattern includes for example indexes from at least two distinct categories.

In optional addition, when the protein database 21 includes several indexes, the best indexes are for example selected by determining at first the best index j1 as above explained, and then by identifying the second best j2 in the remaining set of indexes which corresponds to the initial set of indexes less the best index (determined at first); and so on.

As an example, with the AAIndex including 566 indexes, the 566 indexes are tested one by one. A ranking of the 566 indexes of the protein database 21 is done according the cvRMSE value during a cross-validation procedure. The best index j1 is the one that gives the lowest cvRMSE. Then, the second-best index j2 is identified by testing successively, once again all the (566-1) indexes. At the end of the process, the second index j2 is chosen according to the lowest value of the cvRMSE as obtained using a LOOCV. And so on for a third-best index j3.

The modeling module 24 is adapted for predetermining the protein spectra database 25, also called model, according to learning data and learning extended numerical sequences. The learning extended numerical sequences correspond to the learning data and the learning data are each related to a given fitness, and preferably for different values of said fitness.

The modeling module 24 is further configured for obtaining the reference extended numerical sequences with the same concatenation pattern as the one used by the determination module 22 for concatenating the Q elementary numerical sequences into the extended numerical sequence Ext_SEQ.

The reference database 25 contains reference extended numerical sequences for different values of said fitness. Preferably, at least 10 extended numerical sequences and 10 different fitness are used to build the reference database 25. Of course, the higher are the number of reference extended numerical sequences and related protein fitness; the better will be the results in terms of prediction of fitness.

The prediction module 26 is adapted, for each fitness, for comparing the determined extended numerical sequence Ext_SEQ with reference extended numerical sequences of the reference database 25 and predicting a value of said fitness according to said comparison.

The prediction module 26 is preferably further configured for identifying, in the predetermined database 25 of reference extended numerical sequences for different values of said fitness, the reference extended numerical sequence which is the closest according to a predetermined criterion to the determined extended numerical sequence Ext_SEQ, the predicted value of said fitness being then equal to the fitness value which is associated in said database with the identified reference extended numerical sequence.

The predetermined criterion is, for example, the minimum difference between the determined extended numerical sequence Ext_SEQ and the reference extended numerical sequences contained in the reference database 25. Alternatively, the predetermined criterion is the correlation coefficient R or determination coefficient R2 between the determined extended numerical sequence Ext_SEQ and the reference extended numerical sequences contained in the reference database 25.

Alternatively, the prediction module 26 is configured for computing the predicted value of the fitness using an Artificial Neural Network (ANN), with the input variable being the determined extended numerical sequence Ext_SEQ and the output variable being the predicted value of the fitness. According to this alternative, the Artificial Neural Network is previously trained on the reference extended numerical sequences of the reference database 25 which have the same concatenation pattern as the one used for determining the extended numerical sequence Ext_SEQ.

In addition, in an optional manner, the prediction module 26 allows obtaining a screening of mutants' libraries.

In addition, in an optional manner, the screening module 28 is adapted for analyzing proteins according to the determined extended numerical sequence Ext_SEQ, and for classifying protein sequences according to their respective extended numerical sequence Ext_SEQ using mathematical treatments, such as a factorial discriminant analysis or a principal component analysis followed for example by a k-means. The classification can be done for example to identify if in a family of protein spectra different groups exist: groups with high, intermediate and low fitness; a group with an expression of fitness and a group with no expression of fitness, as examples.

The operation of the electronic prediction system 10 according to the invention will now be described in view of FIG. 2 representing a flow chart of the method for predicting at least one fitness value of a protein.

In an initial step 100, the calculation module 20 calculates several elementary numerical sequences, each elementary numerical sequence depending on a respective encoding of the amino acid sequence of the protein according to the protein database 21.

The calculation module 20 encodes the amino acid sequence into respective elementary numerical sequence(s) according to the protein database 21 by determining, for each amino acid, the numerical value for said amino acid in the given index, for example in the given AAindex code, and then issues an encoded value x_(k) which is equal to said numerical value.

In addition, when the protein database 51 optionally includes several indexes of numerical values; the encoding module 50 further selects the best index based as described above; and then encodes the amino acid sequence using the selected index. The best index is, for example, selected using equation (1) or equation (2).

Alternatively, or in addition, when the protein database 51 optionally includes several indexes of numerical values; the encoding module 50 uses several indexes for encoding several respective elementary numerical sequences.

In optional addition, the calculation module 20 optionally normalizes each obtained elementary numerical sequence, for example by subtracting to each value x_(k) of the numerical sequence a mean x of the numerical sequence values according to equation (3).

In optional addition, the calculation module 20 optionally performs zero-padding on the obtained elementary numerical sequence by adding M zeros at one end of said elementary numerical sequence.

In optional addition, at least one elementary numerical sequence is an elementary protein spectrum, and the calculation module 20 applies accordingly a Fourier Transform, such as a Fast Fourier Transform, to an intermediate numerical sequence obtained by a respective encoding of the amino acid sequence of the protein, in order to obtain the corresponding elementary protein spectrum. The elementary protein spectra f_(j) are preferably calculated by using a Fourier Transform, such as a Fast Fourier Transform, for example according to an equation among the equations (5) to (8) depending on an optional normalization and/or zero-padding.

In the next step 110, the determination module determines the extended numerical sequence Ext_SEQ by concatenating the Q elementary numerical sequences, all the elementary numerical sequences being distinct from each other.

For example, among a pair of elementary numerical sequences, one differs from the other further to the applying of the Fourier Transform for only one elementary numerical sequence of the pair and/or further to a different index from the one to the other elementary numerical sequence of the pair.

The determination module 22 for example determines the extended numerical sequence Ext_SEQ according to the formulation (9) in the case of a single encoding index, or according to any one formulations (10) to (13) in the case of two distinct encoding indexes, or according to similar formulations with at least elementary numerical sequences in the case of more than two distinct encoding indexes.

At the end of the determining step 110, the determination module 22 delivers learning data and learning extended numerical sequences to the modeling module 24.

Then, the modeling module 24 determines, in step 120, the reference database 25 according to learning data and learning extended numerical sequences obtained at the end of the determining step 110.

During the modeling step 120, the modeling module 24 evaluates multiple encoding indexes to find the best for the construction of models. For example, the modeling module 24 therefore uses an initial dataset, also called training dataset, to construct a predictive model for each encoding index. For each model, the modeling module 24 calculates the value of the performance parameters in two stages. A first stage is a standard cross validation. A second stage is a modeling integrating the full set in the learning step. The performances from the two stages are analyzed in order to evaluate and to check the robustness and the validity of the model.

In the first stage, i.e. the cross-validation stage, the initial dataset is split into k equal portions. The number k varies according to the size of the initial dataset. The modeling module 24 uses a low k value if the dataset size is high and conversely a high k value in the opposite situation. The modeling module 24 uses k-1 portions as the learning dataset and the remaining one as the test dataset. This is repeated k more times until each portion is used as the testing dataset once. The cross-validation allows to avoid potential overfitting problem and to optimize some modeling parameters. The cross-validation is for example the Leave-One-Out Cross-Validation (LOOCV), where k is equal to the number Q of elementary numerical sequences.

In the second stage, the full set stage, the whole initial dataset is used as a learning dataset and a test dataset is tested with the optimized parameters from the first stage. In this second stage, the modeling module 24 checks the accuracy of the predictions for learned sequences.

At the end of the modeling step 120, the modeling module 24 selects and stores the reference database 25 a set of accurate models and their associated encoding indexes.

In step 130, for each fitness, the prediction module 26 compares the determined extended numerical sequence Ext_SEQ with reference extended numerical sequences of the reference database 25 and predicts a value of said fitness according to said comparison.

More precisely, the prediction module 26 identifies, in the reference database 25, the reference extended numerical sequence which is the closest according to a predetermined criterion to the determined extended numerical sequence Ext_SEQ, the predicted value of said fitness being then equal to the fitness value which is associated in said database with the identified reference extended numerical sequence.

Alternatively, the prediction module 26 computes the predicted value of the fitness using the Artificial Neural Network (ANN), with the input variable being the determined extended numerical sequence Ext_SEQ and the output variable being the predicted value of the fitness. According to this alternative, the Artificial Neural Network is previously trained on the reference extended numerical sequences of the reference database 25 which have the same concatenation pattern as the one used for determining the extended numerical sequence Ext_SEQ.

Finally, and optionally, the screening module 28 analyzes, in step 140, proteins according to the determined extended numerical sequence Ext_SEQ and classifies protein sequences according to their respective extended numerical sequence Ext_SEQ using mathematical treatments, such as a factorial discriminant analysis or a principal component analysis followed for example by a k-means.

It therefore allows obtaining a better screening of mutants' libraries. This step is also called multivariate analysis step.

It should be noted that the analysis step 140 directly follows the determining step 120 and that in addition the predicting step 130 may be performed after the analysis step 140 for predicting fitness values for some or all of the classified proteins.

EXAMPLES

The invention will be further illustrated in view of the following examples.

In these examples, four datasets have been used: a cytochrome P450 dataset, a GLP2 dataset, an epoxide hydrolase dataset and a TNF dataset.

The versatile cytochrome P450 family of heme-containing redox enzymes hydroxylates a wide range of substrates to generate products of significant medical and industrial importance.

3 parental cytochrome P450, i.e. CYP102A1 (SEQ ID NO: 1), CYP102A2 (SEQ ID NO: 2) and CYP102A3 (SEQ ID NO: 3), were used to generate 184 chimeric sequences of cytochrome P450. For each variant, the thermostability was analyzed by the measurement of the temperature T₅₀, at which 50% of the protein irreversibly denatured after incubation for 10 min. This dataset was disclosed in the article “A diverse family of thermostable cytochrome P450s created by recombination of stabilizing fragments” from Li, Y., Drummond, D. A., Sawayama, A. M., Snow, C. D., Bloom, J. D. & Arnold, F. H., published in 2007 in Nature biotechnology, 25(9), 1051.

TABLE 2 cytochrome P450 learning set Thermostability Chimera (° C.) 11111111 54.9 22222222 43.6 33333333 49.1 32233232 39.8 32313233 52.9 21133233 48.8 31312113 45 21332223 48.3 21312323 61.5 22312322 54.6 21212112 51.2 23133121 47.3 11312233 51.6 21133312 45.4 21133313 50.8 11332233 43.3 31212332 53.4 12211232 49.1 31312133 52.6 12232332 39.2 22133232 47.9 22233221 46.8 23113323 51 11332212 47.8 32332231 49.4 22132331 53.3 23313111 56.9 23112323 46 11113311 51.2 21232233 50.6 12332233 47.1 23333311 45.7 32132233 42.9 22331123 47.9 12212332 48.4 31212323 48.7 32312322 49.1 32312231 52.6 21232332 49.3 31331331 47.3 21132222 45.6 21212333 63.2 21231233 50.6 22212322 50.7 21112122 50.3 22111223 51.3 23233212 39.5 31312212 48.9 32211323 46.6 21213231 54.9 21332312 52.9 22332211 53 22113323 53.8 22113332 48.7 22213132 52 31213332 50.8 22113211 51.1 22313323 60 32333233 47.2 22331223 51.7 23333233 51 22333332 49 23332331 48 21233132 42.4 13333211 45.7 22232331 50.5 22313233 58.5 31311233 56.9 21132321 49.3 32212231 47.4 23212212 48 22113223 49.9 22233211 46.3 23213311 49.5 31212321 44.9 23112233 51 32332323 48.5 22112223 52.8 32313231 52.5 32132232 42.5 22232233 49.6 22232322 45.4 22333211 50.7 22332223 52.4 23213212 49 23333213 50.1 31312233 57.9 22232333 53.7 31333233 46.5 22213212 50.5 22132212 46.6 21332233 58.9 23333131 50.5 31312332 54.9 21333221 51.3 22333223 49.9 21111333 62.4 12212212 44.8 11313233 48.3 32113232 47.9 21113322 50.4 31313232 51.9 23213333 56.1 21333233 54.2 22233212 44 21313112 54.8 31213233 50.6 22132113 40.6 31112333 55.7 31212331 51.8 22232222 47.5 23332221 46.4 21332131 58.5 23231233 45.5 22111332 50.9 23312121 49.3 22332222 50.3 23312323 53.8 21131121 53 32212232 48.8 22112323 55.3 21232232 49.5 11212333 50.4 31212232 51 23213211 47.4 11331312 43.5 23331233 50.9 22133323 49.4 33333233 46.3 22233323 48.4 32232131 43.9 31312323 52.3 21313313 64.4 22333231 53.1 22232123 43.1 21132323 50.1 23332231 51.4 12112333 50.9 22133212 47.2 31113131 54.9 23313333 61.2 21113133 51.9 21111323 54.4 22212123 47.7 12211333 50.6 23113112 46.3 21313122 50.5 23112333 54.3 12213212 44 23132233 43.6 21313311 56.9 21332231 60 23133233 43.1 21132212 48.8 23313233 56.3 21332322 48.8 22132231 53 21113312 53 22312223 56.2 23332223 46.7 32212323 48.4 21212111 57.2 31212212 47.1 22232121 49.7 21232212 47.8 21333223 49.1 23213232 48.5 22113232 51.1 11331333 46.3 22333321 49.2 21232321 46 31332233 49.9 21133232 46.4 22112211 54.7 21333333 58 22213223 50.8 21332112 50.4 21331332 52 11313333 53.8 32311323 52 23132231 48 12232232 40.9 21212231 59.9 33312333 54.7 22313232 58.8 22312111 53 32212233 49.9

The GLP2 dataset involves the potency of 31 alanine variants of the Glucagon like peptide-2 (GLP-2) with respect to the activation of its receptor. GLP-2 (SEQ ID NO: 4) is a short 33 residues peptide whose increase in activity has direct implication in the control of epithelial growth in the intestine. The value for the corresponding receptor activation for the 31 alanine variants of GLP-2 is defined as the fold increase over basal cAMP production and are ranged from 0.7 to 10.4.

TABLE 3 GLP2 learning set Variant fold-increase in cAMP A2G 10.00 H1A 0.78 D3A 1.02 G4A 2.87 S5A 4.00 F6A 1.67 S7A 7.59 D8A 7.53 E9A 3.76 M10A 0.78 N11A 7.65 T12A 3.11 I13A 2.45 L14A 2.03 D15A 4.54 N16A 5.92 L17A 1.02 R20A 3.94 D21A 4.42 F22A 1.61 I23A 2.57 N24A 10.00 W25A 1.37 L26A 2.33 I27A 5.02 Q28A 5.80 T29A 4.78 K30A 0.84 I31A 4.42 T32A 0.84 D33A 3.41

The epoxide hydrolase dataset, disclosed in the article from Reetz, M. T., & Sanchis, J. (2008) “Constructing and analyzing the fitness landscape of an experimental evolutionary process”. ChemBioChem, 9(14), 2260-2267, is a collection of 37 mutants and one WT sequences from Aspergillus niger (WT sequence corresponds to SEQ ID NO: 5) and their enantioselectivity. This enzyme is known for the hydrolysis of glycidyl phenyl ether. The epoxide hydrolase allows the synthesis of important intermediates for the synthesis of beta-blockers, commonly used pharmaceutical drugs in hypertension treatment (lakovou, K., Kazanis, M., Vavayannis, A., Bruni, G., Romeo, M. R., Massarelli, P., . . . & Mori, T. (1999). “Synthesis of oxypropanolamine derivatives of 3,4-dihydro-2H-1, 4-benzoxazine, β-adrenergic affinity, inotropic, chronotropic and coronary vasodilating activities”. European journal of medicinal chemistry, 34(11), 903-917). The study of Reetz et al identifies epoxide mutants with an improved selectivity toward the enantiomer S.

TABLE 4 epoxide hydrolase learning set ΔΔG E- values Variant value (kcal/mol) WT 4 −0.85 L215F 12 −1.5 A217N 7 −1.17 R219S 4 −0.85 L249Y 4 −0.85 T317W 12 −1.5 T318V 4 −0.85 M329P 6 −1.08 L330Y 4 −0.85 C350V 5 −0.97 L215F_A217N_R219S 16.29 −1.68 M329P_L330Y 4.24 −0.87 T317W_T318V 16.29 −1.68 L215F_A217N_R219S_M329P_L330Y 21.25 −1.84 L215F_A217N_R219S_C350V 16.02 −1.67 L215F_A217N_R219S_T317W_T318V 38.01 −2.19 L215F_A217N_R219S_L249Y 24.68 −1.93 M329P_L330Y_C350V 4.46 −0.9 T317W_T318V_M329P_L330Y 8.67 −1.3 L249Y_M329P_L330Y 5.09 −0.98 T317W_T318V_C350V 17.7 −1.73 L249Y_C350V 4.39 −0.89 L249Y_T317W_T318V 22.71 −1.88 L215F_A217N_R219S_M329P_L330Y_C350V 24.27 −1.92 L215F_A217N_R219S_T317W_T318V_M329P_L330Y 35.56 −2.15 L215F_A217N_R219S_L249Y_M329P_L330Y 25.94 −1.96 L215F_A217N_R219S_T317W_T318V_C350V 54.77 −2.41 L215F_A217N_R219S_L249Y_C350V 21.61 −1.85 L215F_A217N_R219S_L249Y_T317W_T318V 51.25 −2.37 T317W_T318V_M329P_L330Y_C350V 12.28 −1.51 L249Y_M329P_L330Y_C350V 4.61 −0.92 L249Y_T317W_T318V_M329P_L330Y 18.3 −1.75 L249Y_T317W_T318V_C350V 18 −1.74 L215F_A217N_R219S_L249Y_M329P_L330Y_C350V 71.45 −2.57 L215F_A217N_R219S_T317W_T318V_M329P_L330Y_C350V 32.19 −2.09 L215F_A217N_R219S_L249Y_T317W_T318V_M329P_L330Y 47.17 −2.32 L215 F_A217N_R219S_L249Y_T317W_T318V_C350V 93.2 −2.73 L215F_A217N_R219S_L249Y_T317W_T318V_M329P_L330Y_C350V 117.6 −2.87

The TNF dataset, disclosed in the article from Mukai Y et al. (J Mol Biol. 2009 Jan. 30; 385(4):1221-9) “Structure-function relationship of tumor necrosis factor (TNF) and its receptor interaction based on 3D structural analysis of a fully active TNFR1-selective TNF mutant”, is a collection of 20 mutants and one WT Tumour Necrosis Factor (TNF) sequences (WT sequence corresponds to SEQ ID NO: 6). TNF is an important cytokine that suppresses carcinogenesis and excludes infectious pathogens to maintain homeostasis. The relative affinity (% Kd) of TNF to its two receptors, TNFR1 and TNFR2 is computed as a single ratio of log₁₀(R1/R2) which ranges from 0 to 2.87, where R1 and R2 are affinities of TNF to TNFR1 and TNFR2 respectively as measured by IC₅₀ assays in ng/ml.

TABLE 5 TNF learning set relative binding Variant affinities WT_157aa 0 K11M_K65S_K90P_K98R_K112N_K128P 0.079 L29I 0.079 A84T_V85H_S86K_Q88P_T89Q 0.544 A84S_V85K_S86T_Q88S_T89H 0.663 L29Q_R32W 0.826 L29K_R31A_R32G_E146S_S147T 0.924 A84S_V85T_S86N_Q88N_T89G 0.869 A84S_V85S_S86H_Q88R_T89F 1.079 A84S_V85P_S86L_Q88P_T89K 1.217 A84T_V85S_S86A_Q88G_T89P 1.23 A84T_V85T_S86A_Q88S_T89G 1.31 A145R_E146T_S147D 1.301 A145K_E146D_S147T 2.87 A145R_E146E_S147T 2.228 A145A_E146D_S147D 1.949 A145A_E146N_S147D 2.462 L29T_R31G_R32Y 0.38 L29T_R31K_R32Y 1.127 L29T_R32F_E146T 2.026 A84S_V85K_S86T_Q88T_T89H 0.924

The encoding indexes used in the following examples are listed in Table 6 hereinafter which defines correspondence between the index number and the name of the index in the AAindex database, while indicating the dataset for which the corresponding encoding index was used in the following examples.

TABLE 6 Index number Index name Dataset 39 Normalized frequency of beta-sheet (Chou-Fasman, 1978b) Cytochrome P450 226 Normalized frequency of beta-sheet from CF (Palau et al., 1981) Cytochrome P450 300 Average relative fractional occurrence in A0(i) (Rackovsky-Scheraga, Cytochrome 1982) P450 343 Information measure for extended (Robson-Suzuki, 1976) Cytochrome P450 450 Hydropathy scale based on self-information values in the two-state model Cytochrome (25% accessibility) (Naderi-Manesh et al., 2001) P450 14 Transfer free energy to surface (Bull-Breese, 1974) Epoxide hydrolase 161 Normalized frequency of beta-sheet, with weights (Levitt, 1978) Epoxide hydrolase 178 Retention coefficient in HPLC, pH7.4 (Meek, 1980) Epoxide hydrolase 232 Normalized frequency of beta-sheet in all-beta class (Palau et al., 1981) Epoxide hydrolase 254 Relative frequency in beta-sheet (Prabhakaran, 1990) Epoxide hydrolase 303 Average relative fractional occurrence in EL(i) (Rackovsky-Scheraga, Epoxide 1982) hydrolase 508 Linker propensity from helical (annotated by DSSP) dataset (George- Epoxide Heringa, 2003) hydrolase 516 Hydrostatic pressure asymmetry index, PAI (Di Giulio, 2005) Epoxide hydrolase 44 Normalized frequency of C-terminal non helical region (Chou-Fasman, GLP2 1978b) 193 AA composition of mt-proteins from animal (Nakashima et al., 1990) GLP2 233 Normalized frequency of beta-sheet in alpha + beta class (Palau et al., GLP2 1981) 341 Information measure for middle helix (Robson-Suzuki, 1976) GLP2 350 Information measure for coil (Robson-Suzuki, 1976) GLP2 440 Distribution of amino acid residues in the 18 non-redundant families of GLP2 thermophilic proteins (Kumar et al., 2000) 449 Hydropathy scale based on self-information values in the two-state model GLP2 (20% accessibility) (Naderi-Manesh et al., 2001) 203 AA composition of CYT2 of single-spanning proteins (Nakashima- TNF Nishikawa, 1992) 297 Average reduced distance for C-alpha (Rackovsky-Scheraga, 1977) TNF 486 Electron-ion interaction potential values (Cosic, 1994) TNF 504 Linker propensity from 3-linker dataset (George-Heringa, 2003) TNF 523 Apparent partition energies calculated from Chothia index (Guy, 1985) TNF

Example 1: Cytochrome P450 (FIGS. 3 and 4)

In the first example, the amino acid sequence of cytochrome P450 was encoded into a numerical sequence using a single encoding index, in particular the one identified with index number 300.

FIG. 3 represents a plot of measured thermostability of cytochrome P450 variants versus thermostability predicted according to the prior art prediction method, using the encoding index identified with index number 300, while applying a Fast Fourier Transform to the encoded numerical sequence. FIG. 3 therefore corresponds to FFT_Seq with index number 300.

FIG. 4 represents a plot of measured thermostability of cytochrome P450 variants versus thermostability predicted with the prediction method according to the invention, using the same encoding index identified with index number 300 for two elementary numerical sequences, one elementary numerical sequence without further applying Fourier Transform to the elementary numerical sequence and the other elementary numerical sequence with further application of the Fast Fourier Transform. FIG. 4 therefore corresponds to the extended numerical sequence Ext_SEQ equal to noFFT_Seq--FFT_Seq with index number 300.

FIG. 3 shows the results obtained for Cytochrome P450 using the best index obtained when a Fast Fourier Transform is applied and with the prior art prediction method: cvR2 and cvRMSE are respectively 0.83 and 1.91.

With the same encoding index, FIG. 4 shows better results obtained with the prediction method according to the invention: cvR2 and cvRMSE are respectively 0.83 and 1.9.

The root mean squared error RMSE, also denoted cvRMSE, and the coefficient of determination R², also denoted cvR2, are performance parameters to assess the regression model of the prediction module 26, during a validation phase with a comparison of the predicted fitness values versus corresponding measured fitness values. RMSE values varies between 0 and +∞. R² value varies between 0 and 1. An accurate regression model has an RMSE close to 0 and a R² close to 1.

Example 2: GLP2 Mutants (FIGS. 5 and 6)

In the second example, the amino acid sequence of GLP2 variants (or mutants) was encoded into a numerical sequence using a single best encoding index (index number 449) for FIG. 5 and using the two best encoding indexes (index numbers 449 and 341) for FIG. 6.

FIG. 5 represents a plot of measured potency (fold-increase in cAMP) of GLP2 variants versus potency predicted according to the prior art prediction method, using the encoding index identified with index number 449, while applying a Fast Fourier Transform to the encoded numerical sequence. FIG. 5 therefore corresponds to FFT_Seq with index number 449.

FIG. 6 represents a plot of measured potency (fold-increase in cAMP) of GLP2 variants versus potency predicted with the prediction method according to the invention, using the two best encoding indexes (index numbers 449 and 341) for two elementary numerical sequences, each one with further application of the Fast Fourier Transform. FIG. 6 therefore corresponds to the extended numerical sequence Ext_SEQ equal to FFT_Seq_(j1)--FFT_Seq_(j2) with j1 equal to index number 449 and j2 equal to index number 341.

FIG. 5 shows the results obtained with the first best index 449 alone: cvR2 and cvRMSE are respectively 0.42 and 2.11.

With the two best encoding indexes (index numbers 449 and 341), FIG. 6 shows significantly better results obtained with the prediction method according to the invention: cvR2 and cvRMSE are respectively 0.55 and 1.77.

Example 3: Epoxide Hydrolase (FIGS. 7 to 9)

In the third example, the amino acid sequence of epoxide hydrolase variants was encoded into a numerical sequence using a single best encoding index (index number 303) for FIG. 7, using a single second-best encoding index (index number 14) for FIG. 8 and using the two best encoding indexes (index numbers 303 and 14) for FIG. 9.

FIG. 7 represents a plot of measured ΔΔG‡ of epoxide hydrolase variants versus ΔΔG‡ predicted according to the prior art prediction method, using the encoding index number 303, while applying a Fast Fourier Transform to the encoded numerical sequence. FIG. 7 therefore corresponds to FFT_Seq with index number 303.

Similarly, FIG. 8 represents a plot of measured ΔΔG‡ of epoxide hydrolase variants versus ΔΔG‡ predicted according to the prior art prediction method, using the encoding index number 14, while applying a Fast Fourier Transform to the encoded numerical sequence. FIG. 8 therefore corresponds to FFT_Seq with index number 14.

FIG. 9 represents a plot of measured ΔΔG‡ of epoxide hydrolase variants versus ΔΔG‡ predicted with the prediction method according to the invention, using the two best encoding indexes (index numbers 303 and 14) for two elementary numerical sequences, each one with further application of the Fast Fourier Transform. FIG. 9 therefore corresponds to the extended numerical sequence Ext_SEQ equal to FFT_Seq_(j1)--FFT_Seq_(j2) with j1 equal to index number 303 and j2 equal to index number 14.

FIG. 7 shows the results obtained with the first best index 303 alone: cvR2 and cvRMSE are respectively 0.96 and 0.12. FIG. 8 shows the results obtained with the second-best index 14 alone: cvR2 and cvRMSE are respectively 0.9 and 0.19.

With the two best encoding indexes (index numbers 303 and 14), FIG. 9 shows slightly better results obtained with the prediction method according to the invention: cvR2 and cvRMSE are respectively 0.97 and 0.1. Thus, using two encoding indexes for obtaining the extended numerical sequence Ext_SEQ improves the quality of the prediction, once again. Even such a slight aforementioned improvement could be important when epistatic phenomenon take place.

Example 4: TNF (FIGS. 10 and 11)

In the fourth example, the amino acid sequence of TNF variants was encoded into a numerical sequence using a single best encoding index (index number 203) for FIG. 10 and using the two best encoding indexes (index numbers 203 and 504) for FIG. 11.

FIG. 10 represents a plot of measured affinity of TNF variants versus affinity predicted according to the prior art prediction method, using the encoding index with number 203, while applying a Fast Fourier Transform to the encoded numerical sequence. FIG. 10 therefore corresponds to FFT_Seq with index number 203.

FIG. 11 represents a plot of measured affinity of TNF variants versus affinity predicted with the prediction method according to the invention, using the two best encoding indexes (index numbers 203 and 504) for two elementary numerical sequences, each one with further application of the Fast Fourier Transform. FIG. 11 therefore corresponds to the extended numerical sequence Ext_SEQ equal to FFT_Seq_(j1)--FFT_Seq_(j2) with j1 equal to index number 203 and j2 equal to index number 504.

FIG. 10 shows the results obtained with the first best index 203 alone: cvR2 and cvRMSE are respectively 0.85 and 0.32.

With the two best encoding indexes (index numbers 203 and 504), FIG. 11 shows better results obtained with the prediction method according to the invention: cvR2 and cvRMSE are respectively 0.87 and 0.29.

Example 5: Cytochrome P450 (FIGS. 12 and 13)

In the fifth example, the amino acid sequence of cytochrome P450 was encoded into a numerical sequence using two encoding indexes, including the best encoding index 300, for FIGS. 12 and 13.

FIG. 12 represents a plot of measured thermostability of cytochrome P450 variants versus thermostability predicted with the prediction method according to the invention, using the two encoding indexes (index numbers 300 and 39) for two elementary numerical sequences, each one with further application of the Fast Fourier Transform. FIG. 12 therefore corresponds to the extended numerical sequence Ext_SEQ equal to FFT_Seq_(j1) --FFT_Seq_(j2) with j1 equal to index number 300 and j2 equal to index number 39.

FIG. 13 represents a plot of measured thermostability of cytochrome P450 variants versus thermostability predicted with the prediction method according to the invention, using the two encoding indexes (index numbers 300 and 343) for two elementary numerical sequences, one (index number 300) with further application of the Fast Fourier Transform and the other one (index number 343) without further application of the Fast Fourier Transform. FIG. 13 therefore corresponds to the extended numerical sequence Ext_SEQ equal to noFFT_Seq_(j1)--FFT_Seq_(j2) with j1 equal to index number 343 and j2 equal to index number 300.

With two encoding indexes (index numbers 300 and 39), FIG. 12 shows significantly better results obtained with the prediction method according to the invention: cvR2 and cvRMSE are respectively 0.87 and 1.67 (in comparison to FIG. 3 for FFT_Seq with best index number 300 where cvR2 and cvRMSE are respectively 0.83 and 1.91).

With two encoding indexes (index numbers 300 and 343), FIG. 13 also shows better results obtained with the prediction method according to the invention: cvR2 and cvRMSE are respectively 0.84 and 1.85 (in comparison to FIG. 3 for FFT_Seq with best index number 300 where cvR2 and cvRMSE are respectively 0.83 and 1.91).

Example 6: Cytochrome P450 (FIG. 14)

In the sixth example, the amino acid sequence of cytochrome P450 was encoded into a numerical sequence using the three best encoding indexes (index numbers 300, 39 and 226) for FIG. 14.

FIG. 14 represents a plot of measured thermostability of cytochrome P450 variants versus thermostability predicted with the prediction method according to the invention, using the three best encoding indexes (index numbers 300, 39 and 226) for three elementary numerical sequences, each one with further application of the Fast Fourier Transform. FIG. 14 therefore corresponds to the extended numerical sequence Ext_SEQ equal to FFT_Seq_(j1)--FFT_Seq_(j2)--FFT_Seq_(j3) with j1 equal to index number 300, j2 equal to index number 39 and j3 equal to index number 226.

With three encoding indexes, FIG. 14 shows significantly better results obtained with the prediction method according to the invention: cvR2 and cvRMSE are respectively 0.88 and 1.63 (in comparison to FIG. 3 for FFT_Seq with best index number 300 where cvR2 and cvRMSE are respectively 0.83 and 1.91).

Example 7: Combinatorial of Multiple Encoding Indexes

Here, the protein sequence is encoded according to n indexes j1 to jn, the n elementary numerical sequences being each one obtained according a respective encoding index. Then a combinatorial is performed in order to find out what is the best combination of m indexes, with m varying from 2 to n.

Each combination is evaluated according to cvRMSE. The best combination corresponds to the lowest cvRMSE. In this case, the best index for one index is not necessarily the best to use for a combination of n indexes.

As an example, with GLP2 variants, the top 10 indexes (after a ranking of the 566 indexes from AAIndex) are kept. A combinatorial of 3 indexes at most is run on the top 10 indexes from the previous ranking. Since FFT_Seq_(j1)--FFT_Seq_(j2) is equivalent to FFT_Seq_(j2)--FFT_Seq_(j1), 175 combined extended sequences are thus obtained.

TABLE 7 Indexes cvRMSE cvR2 440; 350; 44 1.99 0.47 440; 350 1.99 0.47 440; 350; 233 1.99 0.47 440; 44 2.06 0.37 44; 233 2.09 0.37 449; 350 2.10 0.43 449; 350; 233 2.10 0.43 449; 350; 44 2.10 0.43 449 2.11 0.42 449; 233 2.11 0.42

The results obtained with the prior art prediction method with the first best index 449 alone, cvR2 and cvRMSE are respectively 0.42 and 2.11.

Table 7 shows that the best obtained cvR2 and cvRMSE with the prediction method according to the invention with three indexes are respectively 0.47 and 1.99. When ten index are used in order to get FFT_Seq_(j1)--FFT_Seq_(j2)-- . . . --FFT_Seq_(j10), cvRMSE jumps to 2.48 (cvR2=0.11).

Thus, the combinatorial of multiple indexes significantly improves the results. It should be noticed that the right number of indexes has to be determined: a combination of m indexes is not always better than a combination of n indexes with m>n.

Another example with epoxide hydrolase variants leads to similar results according to below Table 8.

TABLE 8 Indexes cvRMSE cvR2 161; 178; 516 0.1051 0.9685 254; 178; 516 0.1051 0.9685 232; 161; 508 0.1123 0.9640 232; 254; 508 0.1123 0.9640 161; 508 0.1146 0.9629 254; 508 0.1146 0.9629 161; 254; 508 0.1150 0.9624 303; 508 0.1161 0.9624 303; 161; 508 0.1170 0.9615 303; 254; 508 0.1170 0.9615

It should be noticed that index 303 identified as the best one when ranking the 566 indexes of AAIndex is classified only in 38^(th) ranking position 38 when a combinatorial is used, i.e. 37 combinations of indexes are better than index 303 alone (when considering only the top 10 in this example and when this best index 303 is included in the top 10).

Example 8: GLP2 Mutants (FIG. 15)

In the eighth example (FIG. 15), the amino acid sequence of GLP2 variants was encoded into a numerical sequence using the three encoding indexes issued from three distinct families: index 449 issued from the ‘other properties’ family; index 341 issued from the ‘alpha and turn propensities’ family; and index 193 issued from the ‘composition’ family.

FIG. 15 represents a plot of measured potency (fold-increase in cAMP) of GLP2 variants versus potency predicted with the prediction method according to the invention, using the three aforementioned encoding indexes (index numbers 449, 341 and 193) for three elementary numerical sequences, each one with further application of the Fast Fourier Transform. FIG. 15 therefore corresponds to the extended numerical sequence Ext_SEQ equal to FFT_Seq_(j1)--FFT_Seq_(j2)--FFT_Seq_(j3) with j1 equal to index number 449, j2 equal to index number 341 and j3 equal to index number 193.

With three encoding indexes, FIG. 15 shows significantly better results obtained with the prediction method according to the invention: cvR2 and cvRMSE are respectively 0.55 and 1.75 (in comparison to FIG. 5 for FFT_Seq with best index number 449 where cvR2 and cvRMSE are respectively 0.42 and 2.11).

Example 9: TNF (FIGS. 16 and 17)

In the ninth example (FIGS. 16 and 17), the amino acid sequence of TNF variants was encoded into a numerical sequence using the three encoding indexes issued from three distinct clusters: index 203 issued from cluster C3; index 504 issued from cluster C8; and index 486 issued from cluster C5 of FIG. 17.

For obtaining the classification into clusters, such as the one shown in FIG. 17, the encoding indexes, such as the 566 indexes of AAIndex, are classified into different clusters using an approach for unsupervised clusterization such a K-means, fuzzy analysis clustering, partitioning around medoids, etc.

Each index is affected to a cluster based on the selected approach. A ranking on the indexes is run and in each cluster one or more top indexes are selected. As an example, a combinatorial as described above could be performed using the top NbC index where one index is chosen in one cluster (NbC=number of clusters). The clustering allows to regroup the indexes by their statistical features rather than by their biological and physicochemical features.

FIG. 16 represents a plot of measured affinity of TNF variants versus affinity predicted with the prediction method according to the invention, using the three aforementioned encoding indexes (index numbers 203, 504 and 486) for three elementary numerical sequences, each one with further application of the Fast Fourier Transform. FIG. 16 therefore corresponds to the extended numerical sequence Ext_SEQ equal to FFT_Seq_(j1)--FFT_Seq_(j2)--FFT_Seq_(j3) with j1 equal to index number 203, j2 equal to index number 504 and j3 equal to index number 486.

With three encoding indexes, FIG. 16 shows significantly better results obtained with the prediction method according to the invention: cvR2 and cvRMSE are respectively 0.88 and 0.28 (in comparison to FIG. 5 for FFT_Seq with best index number 449 where cvR2 and cvRMSE are respectively 0.42 and 2.11).

Example 10: TNF (FIG. 18)

Alternatively, a model is built for each index and selected models (based on the cvRMSE criteria) are used to form ensemble of models in order to calculate for example a mean of the predictions of the hold out sequences with each of these models, or to use the predicted values of each model to build a new model that allow new predictions, or more generally to use different approaches of ensemble modeling such as staking, bagging, boosting.

As an example, 20 models are used based on one index at a time (i.e. 20 different indexes are used), 10 that predict efficiently an upper part of the plot and 10 that predict efficiently a down part of the plot, and a mean of the predictions is computed. The mean of the predictions is then expected to fit in a better way to the diagonal.

TABLE 9 Model Type R2 1 Down model 0.47 2 Down model 0.45 3 Down model 0.63 4 Down model 0.46 5 Down model 0.45 6 Down model 0.51 7 Down model 0.53 8 Down model 0.49 9 Down model 0.41 10 Down model 0.50 11 Upper model 0.47 12 Upper model 0.50 13 Upper model 0.34 14 Upper model 0.56 15 Upper model 0.71 16 Upper model 0.55 17 Upper model 0.61 18 Upper model 0.60 19 Upper model 0.36 20 Upper model 0.33 Ensemble Ensemble of upper 0.83 and down models

Above Table 9 provides such example results for the set of TNF variants.

FIG. 18 represents a plot of measured affinity of TNF variants versus affinity predicted with the prediction method according to the invention, using the aforementioned ensemble.

With the aforementioned ensemble, FIG. 18 shows significantly better results obtained with the prediction method according to the invention: cvR2 and cvRMSE are respectively 0.83 and 0.33 (in comparison to FIG. 5 for FFT_Seq with best index number 449 where cvR2 and cvRMSE are respectively 0.42 and 2.11).

Example 11: TNF (FIGS. 19 to 21)

In the eleventh example, the amino acid sequence of TNF variants was encoded into a numerical sequence using a single first encoding index (index number 523) for FIG. 19 corresponding to an upper model, using a single second encoding index (index number 297) for FIG. 20 corresponding to a down model and using these two aforementioned encoding indexes (index numbers 523 and 297) for FIG. 21.

FIG. 19 represents a plot of measured affinity of TNF variants versus affinity predicted according to the prior art prediction method, using the encoding index number 523, while applying a Fast Fourier Transform to the encoded numerical sequence. FIG. 19 therefore corresponds to FFT_Seq with index number 523.

Similarly, FIG. 20 represents a plot of measured affinity of TNF variants versus affinity predicted according to the prior art prediction method, using the encoding index number 297, while applying a Fast Fourier Transform to the encoded numerical sequence. FIG. 20 therefore corresponds to FFT_Seq with index number 297.

FIG. 21 represents a plot of measured affinity of TNF variants versus affinity predicted with the prediction method according to the invention, using the two aforementioned encoding indexes (index numbers 523 and 297) for two elementary numerical sequences, each one with further application of the Fast Fourier Transform. FIG. 21 therefore corresponds to the extended numerical sequence Ext_SEQ equal to FFT_Seq_(j1)--FFT_Seq_(j2) with j1 equal to index number 523 and j2 equal to index number 297.

FIG. 19 shows the results obtained with the first index 523 alone: cvR2 and cvRMSE are respectively 0.5 and 0.68. FIG. 20 shows the results obtained with the second index 297 alone: cvR2 and cvRMSE are respectively 0.45 and 0.57.

With these two encoding indexes (index numbers 523 and 297), FIG. 21 shows better results obtained with the prediction method according to the invention: cvR2 and cvRMSE are respectively 0.6 and 0.53.

Example 12: Cytochrome P450 (FIGS. 22 to 24)

In optional addition, as previously described, the prediction method according to the invention is applicable on the entire protein sequence as exemplified in the previous examples or on a selection of position(s) in the protein sequence without FFT and/or on a selection of frequencies in the protein spectrum of the FFT.

The selection of position(s) is done in a similar manner than the selection of frequency(ies) or harmonic(s), i.e. by using a filter method or a wrapper method, as previously described.

The twelfth example is an example of this optional feature, wherein the prediction method according to the invention is carried out for a selection of frequencies in the protein spectrum of the FFT, i.e. for a given set of frequency(ies) or harmonic(s), such as one or several selected harmonics corresponding to one or several frequency ranges.

In the twelfth example, the amino acid sequence of cytochrome P450 was encoded into a numerical sequence using a single best encoding index (index number 300) for FIGS. 22 and 23 and using the two best encoding indexes (index numbers 300 and 343) for FIG. 24.

FIG. 22 represents a plot of measured thermostability of cytochrome P450 variants versus thermostability predicted according to the prior art prediction method, using the encoding index with number 300, while applying a Fast Fourier Transform to the encoded numerical sequence, but only for a given set of harmonic(s) representing a part of the whole spectrum. In this example, the set of harmonic(s) represents approximately 20% of the whole considered spectrum. The harmonics are for example numbered from 0 to 256 and the selected harmonics in this example are the following ones: 3; 7; 18; 22; 29; 33; 42; 46; 48; 58; 59; 65; 69; 79; 81; 88; 94; 99; 103; 109; 111; 112; 115; 128; 132; 134; 138; 139; 142; 146; 159; 160; 163; 165; 171; 177; 182; 183; 184; 206; 214; 220; 222; 223; 224; 225; 226; 230; 235; 238; 240; 249. FIG. 10 therefore corresponds to FFT_(20%)_Seq with index number 300 where FFT_(20%) denotes that the Fast Fourier Transform is applied only for a given set of frequency(ies) or harmonic(s) representing 20% of the whole spectrum.

FIG. 23 represents a plot of measured thermostability of cytochrome P450 variants versus thermostability predicted with the prediction method according to the invention, using the same encoding index identified with index number 300 for two elementary numerical sequences, one elementary numerical sequence without further applying Fourier Transform to the elementary numerical sequence and the other elementary numerical sequence with further application of the Fast Fourier Transform, but only for said given set of frequency(ies) or harmonic(s) representing 20% of the whole spectrum. FIG. 23 therefore corresponds to the extended numerical sequence Ext_SEQ equal to noFFT_Seq--FFT_(20%)_Seq with index number 300.

FIG. 24 represents a plot of measured thermostability of cytochrome P450 variants versus thermostability predicted with the prediction method according to the invention, using the two best encoding indexes (index numbers 300 and 343) for two elementary numerical sequences, one (index number 343) without further applying Fourier Transform to the elementary numerical sequence and the other (index number 300) with further application of the Fast Fourier Transform, but only for said given set of frequency(ies) or harmonic(s) representing 20% of the whole spectrum. FIG. 24 therefore corresponds to the extended numerical sequence Ext_SEQ equal to noFFT_Seq_(j1)--FFT_(20%)_Seq_(j2) with j1 equal to index number 343 and j2 equal to index number 300.

FIG. 22 shows the results obtained with the best index 300 alone and FFT_(20%): cvR2 and cvRMSE are respectively 0.66 and 2.68.

With the same encoding index 300, without FFT and with FFT_(20%):, FIG. 23 shows better results obtained with the prediction method according to the invention: cvR2 and cvRMSE are respectively 0.74 and 2.38.

With the two best encoding indexes (index numbers 300 and 343) and FFT_(20%) for index number 300, FIG. 24 shows better results obtained with the prediction method according to the invention: cvR2 and cvRMSE are respectively 0.74 and 2.39.

Thus, R2 and RMSE between the predicted values and the measured values of several fitness, as illustrated in the aforementioned examples, show that the prediction system 10 and method according to the invention allow a more efficient prediction of different fitness values of different proteins or protein variants than the prior art prediction system and method. 

1-15. (canceled)
 16. Method for predicting at least one fitness value of a protein, the method being implemented on a computer and including: calculating Q elementary numerical sequences, Q being an integer greater than or equal to 2, each elementary numerical sequence depending on a respective encoding of the amino acid sequence of the protein according to a protein database, determining an extended numerical sequence by concatenating the Q elementary numerical sequences, for each fitness: comparing the determined extended numerical sequence with reference extended numerical sequences of a predetermined database, said database containing reference extended numerical sequences for different values of said fitness, predicting a value of said fitness according to the comparing.
 17. The method according to claim 16, wherein at least one elementary numerical sequence is an elementary protein spectrum, the elementary protein spectrum being obtained by applying a Fourier Transform to an intermediate numerical sequence, the intermediate numerical sequence being obtained by a respective encoding of the amino acid sequence of the protein.
 18. The method according to claim 17, wherein the Fourier Transform is a Fast Fourier Transform.
 19. The method according to claim 17, wherein at least one elementary protein spectrum is calculated for said amino acid sequence according to a given set of frequency or frequencies.
 20. The method according to claim 17, wherein each elementary protein spectrum depends on the following equation: $f_{j} = {\sum\limits_{k = 0}^{N - 1}{x_{k} \cdot {\exp\left( {\frac{{- 2}i\pi}{N} \cdot j \cdot k} \right)}}}$ where j is an index-number of the elementary protein spectrum f_(j); the intermediate numerical sequence includes N value(s) denoted x_(k), with 0≤k≤N−1 and N≥1; and i defining the imaginary number such that i²=−1.
 21. The method according to claim 16, wherein the protein database includes at least one index of numerical values, each numerical value being given for a respective amino acid; and wherein each encoding of the amino acid sequence of the protein is performed for a respective index, the value in the numerical sequence for each amino acid being equal to the numerical value for said amino acid in the respective index.
 22. The method according to claim 16, wherein all the elementary numerical sequences are distinct from each other.
 23. The method according to claim 21, wherein at least one elementary numerical sequence is an elementary protein spectrum, the elementary protein spectrum being obtained by applying a Fourier Transform to an intermediate numerical sequence, the intermediate numerical sequence being obtained by a respective encoding of the amino acid sequence of the protein; wherein all the elementary numerical sequences are distinct from each other; and wherein, among a pair of elementary numerical sequences, one differs from the other further to the applying of the Fourier Transform for only one elementary numerical sequence of the pair and/or further to a different index from the one to the other elementary numerical sequence of the pair.
 24. The method according to claim 21, wherein the protein database includes several indexes of numerical values, and wherein the method further includes: selecting the best index(es) based on a comparison of measured fitness values for sample proteins with predicted fitness values previously obtained for said sample proteins according to each index; at least one encoding of the amino acid sequence of the protein being then performed using a respective selected index.
 25. The method according to claim 24, wherein, during the selecting, the selected index(es) are the index(es) with the smallest root mean square error(s), wherein the root mean square error for each index verifies the following equation: ${RMSE_{Index_{-}j}} = \sqrt{\sum\limits_{i = 1}^{S}\frac{\left( {y_{i} - {\overset{\hat{}}{y}}_{i,j}} \right)^{2}}{S}}$ where y_(i) is the measured fitness of the i^(th) sample protein, ŷ_(i,j) is the predicted fitness of the i^(th) sample protein with the j^(th) index, and S the number of sample proteins.
 26. The method according to claim 24, wherein, during the selecting, the selected index(es) are the index(es) with the coefficient(s) of determination nearest to 1, wherein the coefficient of determination for each index verifies the following equation: $R_{{Index}\_ j}^{2} = \frac{\left( {\sum\limits_{i = 1}^{S}{\left( {y_{i} - \overset{\_}{y}} \right)\left( {{\overset{\hat{}}{y}}_{i,j} - \overset{\_}{\overset{\hat{}}{y}}} \right)}} \right)^{2}}{\sum\limits_{i = 1}^{S}{\left( {y_{i} - \overset{\_}{y}} \right)^{2}{\sum\limits_{i = 1}^{S}\left( {{\overset{\hat{}}{y}}_{i,j} - \overset{\_}{\overset{\hat{}}{y}}} \right)^{2}}}}$ where y_(i) is the measured fitness of the i^(th) sample protein, ŷ_(i,j) is the predicted fitness of the i^(th) sample protein with the j^(th) index, S the number of sample proteins, y is an average of the measured fitness for the S sample proteins, and {circumflex over (y)} is an average of the predicted fitness for the S sample proteins.
 27. The method according to claim 16, wherein, during the determining, the elementary numerical sequences are concatenated according to a concatenation pattern for determining the extended numerical sequence, the reference extended numerical sequences having being obtained with the same concatenation pattern.
 28. The method according to claim 27, wherein at least one elementary numerical sequence is an elementary protein spectrum, the elementary protein spectrum being obtained by applying a Fourier Transform to an intermediate numerical sequence, the intermediate numerical sequence being obtained by a respective encoding of the amino acid sequence of the protein; wherein the protein database includes at least one index of numerical values, each numerical value being given for a respective amino acid; wherein each encoding of the amino acid sequence of the protein is performed for a respective index, the value in the numerical sequence for each amino acid being equal to the numerical value for said amino acid in the respective index; and wherein the concatenation pattern defines, for each elementary numerical sequence from the succession of the elementary numerical sequences to be concatenated, the respective index and the applying or not of the Fourier Transform.
 29. The method according to claim 27, wherein the protein database includes several indexes classified into distinct categories, and wherein the concatenation pattern includes indexes from at least two categories.
 30. The method according to claim 29, wherein each category is a family associated to a protein feature.
 31. The method according to claim 30, wherein the protein feature is chosen from among the group consisting of: alpha & turn propensities, beta propensity, composition, hydrophobicity, physicochemical property and other protein property.
 32. The method according to claim 29, wherein each category is a cluster of index(es), the clusters being obtained according to statistical feature(s) of the indexes.
 33. The method according to claim 16, wherein the comparing comprises identifying, in the predetermined database of reference extended numerical sequences for different values of said fitness, the reference extended numerical sequence which is the closest according to a predetermined criterion to the determined extended numerical sequence, the predicted value of said fitness being then equal to the fitness value which is associated in said database with the identified reference extended numerical sequence.
 34. A non-transitory computer-readable medium comprising a computer program product including software instructions which, when implemented by a computer, implement a method according to claim
 16. 35. An electronic prediction system for predicting at least one fitness value of a protein, the prediction system including: a calculation module configured for calculating Q elementary numerical sequences, Q being an integer greater than or equal to 2, each elementary numerical sequence depending on a respective encoding of the amino acid sequence of the protein according to a protein database, a determination module configured for determining an extended numerical sequence by concatenating the Q elementary numerical sequences, a prediction module configured for, for each fitness: comparing the determined extended numerical sequence with reference extended numerical sequences of a predetermined database, said database containing reference extended numerical sequences for different values of said fitness, predicting a value of said fitness according to said comparison. 